Showing 1–50 of 263 results for author: Cho, E

SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding

Authors: Jehyeon Bang, Eunyeong Cho, Ranggi Hwang, Jinha Chung, Minsoo Rhu

Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs) by selectively activating parameters. However, its high memory requirements and sub-optimal parameter efficiency pose significant challenges for efficient deployment. Although CPU-offloaded MoE inference systems have been proposed in the literatur… ▽ More The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs) by selectively activating parameters. However, its high memory requirements and sub-optimal parameter efficiency pose significant challenges for efficient deployment. Although CPU-offloaded MoE inference systems have been proposed in the literature, they offer limited efficiency, particularly for large batch sizes. In this work, we propose SpecMoE, a memory-efficient MoE inference system based on our self-assisted speculative decoding algorithm. SpecMoE demonstrates the effectiveness of applying speculative decoding to MoE inference without requiring additional model training or fine-tuning. Our system improves inference throughput by up to $4.30\times$, while significantly reducing bandwidth requirements of both memory and interconnect on memory-constrained systems. △ Less

Submitted 11 April, 2026; originally announced April 2026.

Comments: This is an extended version of our work, which is accepted for publication at the 63rd ACM/IEEE Design Automation Conference (DAC), 2026

An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation

Authors: Kushankur Ghosh, Mehar Klair, Kian Kyars, Euijin Choo, Jörg Sander

Abstract: Provenance graphs model causal system-level interactions from logs, enabling anomaly detectors to learn normal behavior and detect deviations as attacks. However, existing approaches rely on brittle, manually engineered rules to build provenance graphs, lack functional context for system entities, and provide limited support for analyst investigation. We present Auto-Prov, an adaptive, end-to-end… ▽ More Provenance graphs model causal system-level interactions from logs, enabling anomaly detectors to learn normal behavior and detect deviations as attacks. However, existing approaches rely on brittle, manually engineered rules to build provenance graphs, lack functional context for system entities, and provide limited support for analyst investigation. We present Auto-Prov, an adaptive, end-to-end framework that leverages large language models (LLMs) to automatically construct provenance graphs from heterogeneous and evolving logs, embed system-level functional attributes into the graph, enable provenance graph-based anomaly detectors to learn from these enriched graphs, and summarize the detected attacks to assist an analyst's investigation. Auto-Prov clusters unseen log types and efficiently extracts provenance edges and entity-level information via automatically generated rules. It further infers system-level functional context for both known and previously unseen system entities using a combination of LLM inference and behavior-based estimation. Attacks detected by provenance-graph-based anomaly detectors trained on Auto-Prov's graphs are then summarized into natural-language text. We evaluate Auto-Prov with four state-of-the-art provenance graph-based detectors across diverse logs. Results show that Auto-Prov consistently enhances detection performance, generalizes across heterogeneous log formats, and produces stable, interpretable attack summaries that remain robust under system evolution. △ Less

Submitted 17 March, 2026; originally announced March 2026.

Comments: 21 pages, 7 figures

Scrambler: Mixed Boolean Arithmetic Obfuscation Tool Using E-graph and Equality Expansion

Authors: Seoksu Lee, Sangjun An, Eun-Sun Cho

Abstract: We propose Scrambler, and e-graph-based MBA obfuscation tool using Equality Expansion to efficiently generate complex and diverse expressions with equivalence guaranteed by construction. Experiments show Scrambler improves existing tools in expressiveness and complexity. We propose Scrambler, and e-graph-based MBA obfuscation tool using Equality Expansion to efficiently generate complex and diverse expressions with equivalence guaranteed by construction. Experiments show Scrambler improves existing tools in expressiveness and complexity. △ Less

Submitted 6 March, 2026; v1 submitted 3 March, 2026; originally announced March 2026.

Comments: 4 pages, 1 figure, 1 table

PASCAL: A Phase-Aware Scheduling Algorithm for Serving Reasoning-based Large Language Models

Authors: Eunyeong Cho, Jehyeon Bang, Ranggi Hwang, Minsoo Rhu

Abstract: The emergence of reasoning-based LLMs leveraging Chain-of-Thought (CoT) inference introduces new serving challenges, as their extended reasoning phases delay user-visible output and inflate Time-To-First-Token (TTFT). Existing LLM serving frameworks fail to distinguish between reasoning and answering phases, leading to performance degradation under GPU memory constraints. We present PASCAL, a phas… ▽ More The emergence of reasoning-based LLMs leveraging Chain-of-Thought (CoT) inference introduces new serving challenges, as their extended reasoning phases delay user-visible output and inflate Time-To-First-Token (TTFT). Existing LLM serving frameworks fail to distinguish between reasoning and answering phases, leading to performance degradation under GPU memory constraints. We present PASCAL, a phase-aware scheduling algorithm that prioritizes reasoning to reduce TTFT while using controlled preemption and token pacing during answering to preserve Quality-of-Experience (QoE). Our hierarchical scheduler combines instance-level placement with intra-instance execution and enables dynamic migration at phase boundaries to balance load and reduce interference. Across benchmarks using DeepSeek-R1-Distill-Qwen-32B, PASCAL reduces tail TTFT by up to 72% while maintaining answering phase SLO attainment, demonstrating the importance of phase-aware scheduling for reasoning-based LLM deployment. △ Less

Submitted 11 February, 2026; originally announced February 2026.

Comments: Accepted for publication at the 32nd IEEE International Symposium on High-Performance Computer Architecture (HPCA-32), 2026

Static Detection of Core Structures in Tigress Virtualization-Based Obfuscation Using an LLVM Pass

Authors: Sangjun An, Seoksu Lee, Eun-Sun Cho

Abstract: Malware often uses obfuscation to hinder security analysis. Among these techniques, virtualization-based obfuscation is particularly strong because it protects programs by translating original instructions into attacker-defined virtual machine (VM) bytecode, producing long and complex code that is difficult to analyze and deobfuscate. This paper aims to identify the structural components of virtua… ▽ More Malware often uses obfuscation to hinder security analysis. Among these techniques, virtualization-based obfuscation is particularly strong because it protects programs by translating original instructions into attacker-defined virtual machine (VM) bytecode, producing long and complex code that is difficult to analyze and deobfuscate. This paper aims to identify the structural components of virtualization-based obfuscation through static analysis. By examining the execution model of obfuscated code, we define and detect the key elements required for deobfuscation-namely the dispatch routine, handler blocks, and the VM region-using LLVM IR. Experimental results show that, in the absence of compiler optimizations, the proposed LLVM Pass successfully detects all core structures across major virtualization options, including switch, direct, and indirect modes. △ Less

Submitted 22 January, 2026; v1 submitted 19 January, 2026; originally announced January 2026.

Comments: 7 pages, 7figures, An extended version of this work has been submitted to the Journal of KIISC

Solar Open Technical Report

Authors: Sungrae Park, Sanghoon Kim, Jungho Cho, Gyoungjin Gim, Dawoon Jung, Mikyoung Cha, Eunhae Choo, Taekgyu Hong, Minbyul Jeong, SeHwan Joo, Minsoo Khang, Eunwon Kim, Minjeong Kim, Sujeong Kim, Yunsu Kim, Hyeonju Lee, Seunghyun Lee, Sukyung Lee, Siyoung Park, Gyungin Shin, Inseo Song, Wonho Song, Seonghoon Yang, Seungyoun Yi, Sanghoon Yoon , et al. (12 additional authors not shown)

Abstract: We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despite data scarcity for underserved languages, we synthesize 4.5T tokens of high-quality, domain-specific, and RL-oriented data. Se… ▽ More We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despite data scarcity for underserved languages, we synthesize 4.5T tokens of high-quality, domain-specific, and RL-oriented data. Second, we coordinate this data through a progressive curriculum jointly optimizing composition, quality thresholds, and domain coverage across 20 trillion tokens. Third, to enable reasoning capabilities through scalable RL, we apply our proposed framework SnapPO for efficient optimization. Across benchmarks in English and Korean, Solar Open achieves competitive performance, demonstrating the effectiveness of this methodology for underserved language AI development. △ Less

Submitted 11 January, 2026; originally announced January 2026.

Synergistic Computational Approaches for Accelerated Drug Discovery: Integrating Quantum Mechanics, Statistical Thermodynamics, and Quantum Computing

Authors: Farzad Molani, Art E. Cho

Abstract: Accurately predicting protein-ligand binding free energies (BFEs) remains a central challenge in drug discovery, particularly because the most reliable methods, such as free energy perturbation (FEP), are computationally intensive and difficult to scale. Here, we introduce a hybrid quantum-classical framework that combines Mining Minima sampling with quantum mechanically refined ligand partial cha… ▽ More Accurately predicting protein-ligand binding free energies (BFEs) remains a central challenge in drug discovery, particularly because the most reliable methods, such as free energy perturbation (FEP), are computationally intensive and difficult to scale. Here, we introduce a hybrid quantum-classical framework that combines Mining Minima sampling with quantum mechanically refined ligand partial charges, QM/MM interaction evaluation, and variational quantum eigensolver (VQE)-based electronic energy correction. This design enables explicit treatment of polarization, charge redistribution, and electronic correlation effects that are often underestimated in purely classical scoring schemes, while retaining computational efficiency. Across 23 protein targets and 543 ligands, the method achieves a mean absolute error of about 1.10 kcal/mol with strong rank-order fidelity (Pearson R = 0.75, Spearman rho = 0.76, Kendall tau = 0.57), consistent with the performance of contemporary FEP protocols. Notably, the workflow requires only about 25 minutes per ligand on standard compute resources, resulting in an approximate 20-fold reduction in computational cost relative to alchemical free energy approaches. This level of accuracy and efficiency makes the method well-suited for high-throughput lead optimization and iterative design cycles in pharmaceutical discovery. The framework also provides a natural foundation for future integration with machine learning models to enable predictive, large-scale, and adaptive screening strategies. △ Less

Submitted 5 December, 2025; originally announced December 2025.

FiCABU: A Fisher-Based, Context-Adaptive Machine Unlearning Processor for Edge AI

Authors: Eun-Su Cho, Jongin Choi, Jeongmin Jin, Jae-Jin Lee, Woojoo Lee

Abstract: Machine unlearning, driven by privacy regulations and the "right to be forgotten", is increasingly needed at the edge, yet server-centric or retraining-heavy methods are impractical under tight computation and energy budgets. We present FiCABU (Fisher-based Context-Adaptive Balanced Unlearning), a software-hardware co-design that brings unlearning to edge AI processors. FiCABU combines (i) Context… ▽ More Machine unlearning, driven by privacy regulations and the "right to be forgotten", is increasingly needed at the edge, yet server-centric or retraining-heavy methods are impractical under tight computation and energy budgets. We present FiCABU (Fisher-based Context-Adaptive Balanced Unlearning), a software-hardware co-design that brings unlearning to edge AI processors. FiCABU combines (i) Context-Adaptive Unlearning, which begins edits from back-end layers and halts once the target forgetting is reached, with (ii) Balanced Dampening, which scales dampening strength by depth to preserve retain accuracy. These methods are realized in a full RTL design of a RISC-V edge AI processor that integrates two lightweight IPs for Fisher estimation and dampening into a GEMM-centric streaming pipeline, validated on an FPGA prototype and synthesized in 45 nm for power analysis. Across CIFAR-20 and PinsFaceRecognition with ResNet-18 and ViT, FiCABU achieves random-guess forget accuracy while matching the retraining-free Selective Synaptic Dampening (SSD) baseline on retain accuracy, reducing computation by up to 87.52 percent (ResNet-18) and 71.03 percent (ViT). On the INT8 hardware prototype, FiCABU further improves retain preservation and reduces energy to 6.48 percent (CIFAR-20) and 0.13 percent (PinsFaceRecognition) of the SSD baseline. In sum, FiCABU demonstrates that back-end-first, depth-aware unlearning can be made both practical and efficient for resource-constrained edge AI devices. △ Less

Submitted 6 November, 2025; originally announced November 2025.

Comments: 8 pages, 6 figures, 4 tables, DATE 2026 accepted paper

Experimental confirmation of the magnetic ordering transition induced by an electronic structure change in the metallic triangular antiferromagnet Co$_{1/3}$TaS$_2$

Authors: Han-Jin Noh, En-Jin Cho, Byeong-Gyu Park, Hyowon Park, Ivar Martin, Cristian D. Batista, Pyeongjae Park, Woonghee Cho, Je-Guen Park

Abstract: We report ARPES studies combined with DFT+DMFT calculations to confirm that the magnetic ordering vector transition from \textbf{Q}=(1/2,0,0) to \textbf{Q}=(1/3,0,0) in the metallic triangular antiferromagnets Co$_{1/3\pmε}$TaS$_2$ ($ε\approx$0.007) is induced by the electronic structure change in the system. The ARPES-measured Fermi surface (FS) maps of Co$_{0.325}$TaS$_2$ show two hexagonal and… ▽ More We report ARPES studies combined with DFT+DMFT calculations to confirm that the magnetic ordering vector transition from \textbf{Q}=(1/2,0,0) to \textbf{Q}=(1/3,0,0) in the metallic triangular antiferromagnets Co$_{1/3\pmε}$TaS$_2$ ($ε\approx$0.007) is induced by the electronic structure change in the system. The ARPES-measured Fermi surface (FS) maps of Co$_{0.325}$TaS$_2$ show two hexagonal and one circular hole-like FSs around $Γ$, which matches well with the triple-\textbf{Q} state by taking into account the contribution of nesting vectors occurring between Co 3$d$ and Ta 5$d$ orbitals. In the case of Co$_{0.340}$TaS$_2$, a new electron pocket around K appears and the FS geometry changes as a result of the correlation effect of Co$_4$S$_{18}$ tripods forming in the system. The magnetic susceptibility calculations based on the charge-self-consistent DFT+DMFT band structures and the random phase approximation indicate that the most stable magnetic ordering vector (1/2,0,0) split into (1/6,0,0) and (1/2,0,0), which is consistent with the magnetic phase transition around $x$=1/3 in Co$_{x}$TaS$_2$. △ Less

Submitted 22 February, 2026; v1 submitted 5 November, 2025; originally announced November 2025.

Comments: 5 figures

Acoustic-based Gender Differentiation in Speech-aware Language Models

Authors: Junhyuk Choi, Jihwan Seol, Nayeon Kim, Chanhee Cho, EunBin Cho, Bugeun Kim

Abstract: Speech-aware Language Models (SpeechLMs) have fundamentally transformed human-AI interaction by enabling voice-based communication, yet they may exhibit acoustic-based gender differentiation where identical questions lead to different responses based on the speaker's gender. This paper propose a new dataset that enables systematic analysis of this phenomenon, containing 9,208 speech samples across… ▽ More Speech-aware Language Models (SpeechLMs) have fundamentally transformed human-AI interaction by enabling voice-based communication, yet they may exhibit acoustic-based gender differentiation where identical questions lead to different responses based on the speaker's gender. This paper propose a new dataset that enables systematic analysis of this phenomenon, containing 9,208 speech samples across three categories: Gender-Independent, Gender-Stereotypical, and Gender-Dependent. We further evaluated LLaMA-Omni series and discovered a paradoxical pattern; while overall responses seems identical regardless of gender, the pattern is far from unbiased responses. Specifically, in Gender-Stereotypical questions, all models consistently exhibited male-oriented responses; meanwhile, in Gender-Dependent questions where gender differentiation would be contextually appropriate, models exhibited responses independent to gender instead. We also confirm that this pattern does not result from neutral options nor perceived gender of a voice. When we allow neutral response, models tends to respond neutrally also in Gender-Dependent questions. The paradoxical pattern yet retains when we applied gender neutralization methods on speech. Through comparison between SpeechLMs with corresponding backbone LLMs, we confirmed that these paradoxical patterns primarily stem from Whisper speech encoders, which generates male-oriented acoustic tokens. These findings reveal that current SpeechLMs may not successfully remove gender biases though they prioritized general fairness principles over contextual appropriateness, highlighting the need for more sophisticated techniques to utilize gender information properly in speech technology. △ Less

Submitted 25 September, 2025; originally announced September 2025.

Comments: Under Review

On Alon-Tarsi orientations of sparse graphs

Authors: Eun-Kyung Cho, Ilkyoo Choi, Boram Park, Xuding Zhu

Abstract: Assume $G$ is a graph, $(v_1,\ldots,v_k)$ is a sequence of distinct vertices of $G$, and $(a_1,\ldots,a_k)$ is an integer sequence with $a_i \in \{1,2\}$. We say $G$ is \emph{$(a_1,\ldots,a_k)$-list extendable} (respectively, \emph{$(a_1,\ldots,a_k)$-AT extendable}) with respect to $(v_1,\ldots,v_k)$ if $G$ is $f$-choosable (respectively, $f$-AT), where $f(v_i)=a_i $ for $i \in \{1,\ldots, k\}$, a… ▽ More Assume $G$ is a graph, $(v_1,\ldots,v_k)$ is a sequence of distinct vertices of $G$, and $(a_1,\ldots,a_k)$ is an integer sequence with $a_i \in \{1,2\}$. We say $G$ is \emph{$(a_1,\ldots,a_k)$-list extendable} (respectively, \emph{$(a_1,\ldots,a_k)$-AT extendable}) with respect to $(v_1,\ldots,v_k)$ if $G$ is $f$-choosable (respectively, $f$-AT), where $f(v_i)=a_i $ for $i \in \{1,\ldots, k\}$, and $f(v)=3$ for $v \in V(G) \setminus \{v_1,\ldots, v_k\}$. Hutchinson proved that if $G$ is an outerplanar graph, then $G$ is $(2,2)$-list extendable with respect to $(x,y)$ for any vertices $x,y$. We strengthen this result and prove that if $G$ is a $K_4$-minor-free graph, then $G$ is $(2,2)$-AT extendable with respect to $(x,y)$ for any vertices $x,y$. Then we characterize all triples $(x,y,z)$ of a $K_4$-minor-free graph $G$ for which $G$ is $(2,2,2)$-AT extendable (as well as $(2,2,2)$-list extendable) with respect to $(x,y,z)$. We also characterize the pairs $(x,y)$ of a $K_4$-minor-free graph $G$ for which $G$ is $(2,1)$-AT extendable (as well as $(2,1)$-list extendable) with respect to $(x,y)$. Moreover, we characterize all triples $(x,y,z)$ of a 3-colorable graph $G$ with its maximum average degree less than $\frac{14}{5}$ for which $G$ is $(2,2,2)$-AT extendable with respect to $(x,y,z)$. △ Less

Submitted 30 August, 2025; originally announced September 2025.

Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization

Authors: Eunjung Cho, Alexander Hoyle, Yoan Hermstrüwer

Abstract: Large Language Models (LLMs) are increasingly used to generate user-tailored summaries, adapting outputs to specific stakeholders. In legal contexts, this raises important questions about motivated reasoning -- how models strategically frame information to align with a stakeholder's position within the legal system. Building on theories of legal realism and recent trends in legal practice, we inve… ▽ More Large Language Models (LLMs) are increasingly used to generate user-tailored summaries, adapting outputs to specific stakeholders. In legal contexts, this raises important questions about motivated reasoning -- how models strategically frame information to align with a stakeholder's position within the legal system. Building on theories of legal realism and recent trends in legal practice, we investigate how LLMs respond to prompts conditioned on different legal roles (e.g., judges, prosecutors, attorneys) when summarizing judicial decisions. We introduce an evaluation framework grounded in legal fact and reasoning inclusion, also considering favorability towards stakeholders. Our results show that even when prompts include balancing instructions, models exhibit selective inclusion patterns that reflect role-consistent perspectives. These findings raise broader concerns about how similar alignment may emerge as LLMs begin to infer user roles from prior interactions or context, even without explicit role instructions. Our results underscore the need for role-aware evaluation of LLM summarization behavior in high-stakes legal settings. △ Less

Submitted 8 October, 2025; v1 submitted 30 August, 2025; originally announced September 2025.

Comments: Accepted at NLLP 2025

Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge

Authors: Tobias Rueckert, David Rauber, Raphaela Maerkl, Leonard Klausmann, Suemeyye R. Yildiran, Max Gutbrod, Danilo Weber Nunes, Alvaro Fernandez Moreno, Imanol Luengo, Danail Stoyanov, Nicolas Toussaint, Enki Cho, Hyeon Bae Kim, Oh Sung Choo, Ka Young Kim, Seong Tae Kim, Gonçalo Arantes, Kehan Song, Jianjun Zhu, Junchen Xiong, Tingyi Lin, Shunsuke Kikuchi, Hiroki Matsuzaki, Atsushi Kouno, João Renato Ribeiro Manesco , et al. (36 additional authors not shown)

Abstract: Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical con… ▽ More Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. △ Less

Submitted 19 January, 2026; v1 submitted 22 July, 2025; originally announced July 2025.

Comments: A challenge report pre-print accepted by the journal Medical Image Analysis (MedIA), containing 37 pages, 15 figures, and 14 tables

Towards Holistic Surgical Scene Graph

Authors: Jongmin Shin, Enki Cho, Ka Young Kim, Jung Yong Kim, Seong Tae Kim, Namkee Oh

Abstract: Surgical scene understanding is crucial for computer-assisted intervention systems, requiring visual comprehension of surgical scenes that involves diverse elements such as surgical tools, anatomical structures, and their interactions. To effectively represent the complex information in surgical scenes, graph-based approaches have been explored to structurally model surgical entities and their rel… ▽ More Surgical scene understanding is crucial for computer-assisted intervention systems, requiring visual comprehension of surgical scenes that involves diverse elements such as surgical tools, anatomical structures, and their interactions. To effectively represent the complex information in surgical scenes, graph-based approaches have been explored to structurally model surgical entities and their relationships. Previous surgical scene graph studies have demonstrated the feasibility of representing surgical scenes using graphs. However, certain aspects of surgical scenes-such as diverse combinations of tool-action-target and the identity of the hand operating the tool-remain underexplored in graph-based representations, despite their importance. To incorporate these aspects into graph representations, we propose Endoscapes-SG201 dataset, which includes annotations for tool-action-target combinations and hand identity. We also introduce SSG-Com, a graph-based method designed to learn and represent these critical elements. Through experiments on downstream tasks such as critical view of safety assessment and action triplet recognition, we demonstrated the importance of integrating these essential scene graph components, highlighting their significant contribution to surgical scene understanding. The code and dataset are available at https://github.com/ailab-kyunghee/SSG-Com △ Less

Submitted 23 July, 2025; v1 submitted 21 July, 2025; originally announced July 2025.

Comments: Accepted to MICCAI 2025

Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing

Authors: Eunbyeol Cho, Jiyoun Kim, Minjae Lee, Sungjin Park, Edward Choi

Abstract: Electronic Health Records (EHR) are time-series relational databases that record patient interactions and medical events over time, serving as a critical resource for healthcare research and applications. However, privacy concerns and regulatory restrictions limit the sharing and utilization of such sensitive data, necessitating the generation of synthetic EHR datasets. Unlike previous EHR synthes… ▽ More Electronic Health Records (EHR) are time-series relational databases that record patient interactions and medical events over time, serving as a critical resource for healthcare research and applications. However, privacy concerns and regulatory restrictions limit the sharing and utilization of such sensitive data, necessitating the generation of synthetic EHR datasets. Unlike previous EHR synthesis methods, which typically generate medical records consisting of expert-chosen features (e.g. a few vital signs or structured codes only), we introduce RawMed, the first framework to synthesize multi-table, time-series EHR data that closely resembles raw EHRs. Using text-based representation and compression techniques, RawMed captures complex structures and temporal dynamics with minimal preprocessing. We also propose a new evaluation framework for multi-table time-series synthetic EHRs, assessing distributional similarity, inter-table relationships, temporal dynamics, and privacy. Validated on two open-source EHR datasets, RawMed outperforms baseline models in fidelity and utility. The code is available at https://github.com/eunbyeol-cho/RawMed. △ Less

Submitted 2 March, 2026; v1 submitted 9 July, 2025; originally announced July 2025.

Comments: Accepted at NeurIPS 2025

gMBA: Expression Semantic Guided Mixed Boolean-Arithmetic Deobfuscation Using Transformer Architectures

Authors: Youjeong Noh, Joon-Young Paik, Jingun Kwon, Eun-Sun Cho

Abstract: Mixed Boolean-Arithmetic (MBA) obfuscation protects intellectual property by converting programs into forms that are more complex to analyze. However, MBA has been increasingly exploited by malware developers to evade detection and cause significant real-world problems. Traditional MBA deobfuscation methods often consider these expressions as part of a black box and overlook their internal semanti… ▽ More Mixed Boolean-Arithmetic (MBA) obfuscation protects intellectual property by converting programs into forms that are more complex to analyze. However, MBA has been increasingly exploited by malware developers to evade detection and cause significant real-world problems. Traditional MBA deobfuscation methods often consider these expressions as part of a black box and overlook their internal semantic information. To bridge this gap, we propose a truth table, which is an automatically constructed semantic representation of an expression's behavior that does not rely on external resources. The truth table is a mathematical form that represents the output of expression for all possible combinations of input. We also propose a general and extensible guided MBA deobfuscation framework (gMBA) that modifies a Transformer-based neural encoder-decoder Seq2Seq architecture to incorporate this semantic guidance. Experimental results and in-depth analysis show that integrating expression semantics significantly improves performance and highlights the importance of internal semantic expressions in recovering obfuscated code to its original form. △ Less

Submitted 30 June, 2025; originally announced June 2025.

Spatial Disparities in Fire Shelter Accessibility: Capacity Challenges in the Palisades and Eaton Fires

Authors: Su Yeon Han, Yubin Lee, Jooyoung Yoo, Jeon-Young Kang, Jinwoo Park, Soe W. Myint, Eunsang Cho, Xin Gu, Joon-Seok Kim

Abstract: The increasing frequency and severity of wildfire in California, exacerbated by prolonged drought and environmental changes, pose significant challenges to urban community resilience and equitable emergency response. The study investigates issues of accessibility to shelters during the Palisades and Eaton Fires which started in January 2025 in Southern California that led to over 180,000 displacem… ▽ More The increasing frequency and severity of wildfire in California, exacerbated by prolonged drought and environmental changes, pose significant challenges to urban community resilience and equitable emergency response. The study investigates issues of accessibility to shelters during the Palisades and Eaton Fires which started in January 2025 in Southern California that led to over 180,000 displacements and the loss of 16,000 structures. Despite coordinated efforts of many organizations' emergency assistance, shelter shortages left many evacuees without safety or accessible refuge. This research aims to measure shelter accessibility during the fires' peak, evaluate whether existing shelter capacity met the demand, and identify spatial disparities in access. Findings reveal severe shelter shortages and pronounced inequities in access to shelters, particularly in geographically isolated regions and mountainous areas. To address these challenges, we implemented shelter placement strategies using both capacity-based and distance-based approaches, demonstrating potential improvements in accessibility and equity. The findings underscore the critical need for strategic shelter planning and infrastructure development to enhance disaster readiness and reduce vulnerability in regions that frequently experience wildfires. △ Less

Submitted 16 March, 2026; v1 submitted 7 June, 2025; originally announced June 2025.

Comments: 41 pages, 11 figures

NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment

Authors: Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazarova, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg, David Tomás, Steven R Wilson, Bowen Yi , et al. (8 additional authors not shown)

Abstract: Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summarizing principal tasks and challenges. We analyze ACL Anthology trends, finding that inclusion and AI harms attract the most researc… ▽ More Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summarizing principal tasks and challenges. We analyze ACL Anthology trends, finding that inclusion and AI harms attract the most research, while domains such as poverty, peacebuilding, and environmental protection remain underexplored. Guided by our review, we outline opportunities for responsible and equitable NLP and conclude with a call for cross-disciplinary partnerships and human-centered approaches to ensure that future NLP technologies advance the public good. △ Less

Submitted 21 January, 2026; v1 submitted 28 May, 2025; originally announced May 2025.

Comments: Accepted to EACL 2026

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption

Authors: Abdullah Al Omar, Xin Yang, Euijin Choo, Omid Ardakanian

Abstract: Federated Learning (FL) is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other clients' datasets from their model updates or gradients. To enhance the privacy of FL, recent studies combined Multi-Key Homomorphic Encryption (MKHE) and FL, making it possible to aggregate the encrypted model updates us… ▽ More Federated Learning (FL) is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other clients' datasets from their model updates or gradients. To enhance the privacy of FL, recent studies combined Multi-Key Homomorphic Encryption (MKHE) and FL, making it possible to aggregate the encrypted model updates using different keys without having to decrypt them. Despite the privacy guarantees of MKHE, existing approaches are not well-suited for real-world deployment due to their high computation and communication overhead. We propose MASER, an efficient MKHE-based Privacy-Preserving FL framework that combines consensus-based model pruning and slicing techniques to reduce this overhead. Our experimental results show that MASER is 3.03 to 8.29 times more efficient than existing MKHE-based FL approaches in terms of computation and communication overhead while maintaining comparable classification accuracy to standard FL algorithms. Compared to a vanilla FL algorithm, the overhead of MASER is only 1.48 to 5 times higher, striking a good balance between privacy, accuracy, and efficiency in both IID and non-IID settings. △ Less

Submitted 20 May, 2025; originally announced May 2025.

Bayesian model-averaging stochastic item selection for adaptive testing

Authors: Tina Su, Edison Choe, Joshua C. Chang

Abstract: Computer Adaptive Testing (CAT) aims to accurately estimate an individual's ability using only a subset of an Item Response Theory (IRT) instrument. Many applications also require diverse item exposure across testing sessions, preventing any single item from being over- or underutilized. In CAT, items are selected sequentially based on a running estimate of a respondent's ability. Prior methods al… ▽ More Computer Adaptive Testing (CAT) aims to accurately estimate an individual's ability using only a subset of an Item Response Theory (IRT) instrument. Many applications also require diverse item exposure across testing sessions, preventing any single item from being over- or underutilized. In CAT, items are selected sequentially based on a running estimate of a respondent's ability. Prior methods almost universally see item selection through an optimization lens, motivating greedy item selection procedures. While efficient, these deterministic methods tend to have poor item exposure. Existing stochastic methods for item selection are ad-hoc, with item sampling weights that lack theoretical justification. We formulate stochastic CAT as a Bayesian model averaging problem. We seek item sampling probabilities, treated in the long-run frequentist sense, that perform optimal model averaging for the ability estimate in a Bayesian sense. The derivation yields an information criterion for optimal stochastic mixing: the expected entropy of the next posterior. We tested our method on seven publicly available psychometric instruments spanning personality, social attitudes, narcissism, and work preferences, in addition to the eight scales of the Work Disability Functional Assessment Battery. Across all instruments, accuracy differences between selection methods at a given test length are varied but minimal relative to the natural noise in ability estimation; however, the stochastic selector achieves full item bank exposure, resolving the longstanding tradeoff between measurement efficiency and item security at negligible accuracy cost. △ Less

Submitted 30 March, 2026; v1 submitted 21 April, 2025; originally announced April 2025.

Comments: Under review; major revision

Command A: An Enterprise-Ready Large Language Model

Authors: Team Cohere, :, Aakanksha, Arash Ahmadian, Marwan Ahmed, Jay Alammar, Milad Alizadeh, Yazeed Alnumay, Sophia Althammer, Arkady Arkhangorodsky, Viraat Aryabumi, Dennis Aumiller, Raphaël Avalos, Zahara Aviv, Sammie Bae, Saurabh Baji, Alexandre Barbet, Max Bartolo, Björn Bebensee, Neeral Beladia, Walter Beller-Morales, Alexandre Bérard, Andrew Berneshawi, Anna Bialas, Phil Blunsom , et al. (205 additional authors not shown)

Abstract: In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised and multilingual-capable model, with support for 23 languages of global business, and a novel hybrid architecture balancing efficiency with top of the range performance. It offers best-in-class Retrieval Augmented Genera… ▽ More In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised and multilingual-capable model, with support for 23 languages of global business, and a novel hybrid architecture balancing efficiency with top of the range performance. It offers best-in-class Retrieval Augmented Generation (RAG) capabilities with grounding and tool use to automate sophisticated business processes. These abilities are achieved through a decentralised training approach, including self-refinement algorithms and model merging techniques. We also include results for Command R7B which shares capability and architectural similarities to Command A. Weights for both models have been released for research purposes. This technical report details our original training pipeline and presents an extensive evaluation of our models across a suite of enterprise-relevant tasks and public benchmarks, demonstrating excellent performance and efficiency. △ Less

Submitted 14 April, 2025; v1 submitted 1 April, 2025; originally announced April 2025.

Comments: 55 pages

Obstructions for homomorphisms to odd cycles in series-parallel graphs

Authors: Eun-Kyung Cho, Ilkyoo Choi, Boram Park, Mark Siggers

Abstract: For a graph $H$, an $H$-colouring of a graph $G$ is a vertex map $φ:V(G) \to V(H)$ such that adjacent vertices are mapped to adjacent vertices. A graph $G$ is $C_{2k+1}$-critical if $G$ has no $C_{2k+1}$-colouring but every proper subgraph of $G$ has a $C_{2k+1}$-colouring. We prove a structural characterisation of $C_{2k+1}$-critical graphs when $k \geq 2$. In the case that $k = 2$, we use the af… ▽ More For a graph $H$, an $H$-colouring of a graph $G$ is a vertex map $φ:V(G) \to V(H)$ such that adjacent vertices are mapped to adjacent vertices. A graph $G$ is $C_{2k+1}$-critical if $G$ has no $C_{2k+1}$-colouring but every proper subgraph of $G$ has a $C_{2k+1}$-colouring. We prove a structural characterisation of $C_{2k+1}$-critical graphs when $k \geq 2$. In the case that $k = 2$, we use the aforementioned charazterisation to show a $C_3$-free series-parallel graph $G$ has a $C_5$-colouring if either $G$ has neither $C_8$ nor $C_{10}$, or $G$ has no two $5$-cycles sharing a vertex. △ Less

Submitted 25 March, 2025; originally announced March 2025.

Lost in Edits? A $λ$-Compass for AIGC Provenance

Authors: Wenhao You, Bryan Hooi, Yiwei Wang, Euijin Choo, Ming-Hsuan Yang, Junsong Yuan, Zi Huang, Yujun Cai

Abstract: Recent advancements in diffusion models have driven the growth of text-guided image editing tools, enabling precise and iterative modifications of synthesized content. However, as these tools become increasingly accessible, they also introduce significant risks of misuse, emphasizing the critical need for robust attribution methods to ensure content authenticity and traceability. Despite the creat… ▽ More Recent advancements in diffusion models have driven the growth of text-guided image editing tools, enabling precise and iterative modifications of synthesized content. However, as these tools become increasingly accessible, they also introduce significant risks of misuse, emphasizing the critical need for robust attribution methods to ensure content authenticity and traceability. Despite the creative potential of such tools, they pose significant challenges for attribution, particularly in adversarial settings where edits can be layered to obscure an image's origins. We propose LambdaTracer, a novel latent-space attribution method that robustly identifies and differentiates authentic outputs from manipulated ones without requiring any modifications to generative or editing pipelines. By adaptively calibrating reconstruction losses, LambdaTracer remains effective across diverse iterative editing processes, whether automated through text-guided editing tools such as InstructPix2Pix and ControlNet or performed manually with editing software such as Adobe Photoshop. Extensive experiments reveal that our method consistently outperforms baseline approaches in distinguishing maliciously edited images, providing a practical solution to safeguard ownership, creativity, and credibility in the open, fast-evolving AI ecosystems. △ Less

Submitted 5 February, 2025; originally announced February 2025.

High-dimensional point forecast combinations for emergency department demand

Authors: Peihong Guo, Wen Ye Loh, Kenwin Maung, Esther Li Wen Choo, Borame Lee Dickens, Kelvin Bryan Tan, John Abishgenadan, Pei Ma, Jue Tao Lim

Abstract: Current work on forecasting emergency department (ED) admissions focuses on disease aggregates or singular disease types. However, given differences in the dynamics of individual diseases, it is unlikely that any single forecasting model would accurately account for each disease and for all time, leading to significant forecast model uncertainty. Yet, forecasting models for ED admissions to-date d… ▽ More Current work on forecasting emergency department (ED) admissions focuses on disease aggregates or singular disease types. However, given differences in the dynamics of individual diseases, it is unlikely that any single forecasting model would accurately account for each disease and for all time, leading to significant forecast model uncertainty. Yet, forecasting models for ED admissions to-date do not explore the utility of forecast combinations to improve forecast accuracy and stability. It is also unknown whether improvements in forecast accuracy can be yield from (1) incorporating a large number of environmental and anthropogenic covariates or (2) forecasting total ED causes by aggregating cause-specific ED forecasts. To address this gap, we propose high-dimensional forecast combination schemes to combine a large number of forecasting individual models for forecasting cause-specific ED admissions over multiple causes and forecast horizons. We use time series data of ED admissions with an extensive set of explanatory lagged variables at the national level, including meteorological/ambient air pollutant variables and ED admissions of all 16 causes studied. We show that the simple forecast combinations yield forecast accuracies of around 3.81%-23.54% across causes. Furthermore, forecast combinations outperform individual forecasting models, in more than 50% of scenarios (across all ED admission categories and horizons) in a statistically significant manner. Inclusion of high-dimensional covariates and aggregating cause-specific forecasts to provide all-cause ED forecasts provided modest improvements in forecast accuracy. Forecasting cause-specific ED admissions can provide fine-scale forward guidance on resource optimization and pandemic preparedness and forecast combinations can be used to hedge against model uncertainty when forecasting across a wide range of admission categories. △ Less

Submitted 20 January, 2025; originally announced January 2025.

Journal ref: BMC Emerg Med 26, 83 (2026)

Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study of LLM Hallucination on North Korea

Authors: Eunjung Cho, Won Ik Cho, Soomin Seo

Abstract: Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confidence (or lack thereof) in their outputs. While these measures may be effective i… ▽ More Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confidence (or lack thereof) in their outputs. While these measures may be effective in most contexts, they may fall short in scenarios requiring more nuanced approaches, especially in situations where access to accurate data is limited or determining credible sources is challenging. In this study, we take North Korea - a country characterised by an extreme lack of reliable sources and the prevalence of sensationalist falsehoods - as a case study. We explore and evaluate how some of the best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China). Our findings reveal significant differences, suggesting that the choice of model and language can lead to vastly different understandings of North Korea, which has important implications given the global security challenges the country poses. △ Less

Submitted 10 January, 2025; originally announced January 2025.

Comments: Accepted at COLING 2025

Debunking the CUDA Myth Towards GPU-based AI Systems

Authors: Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu

Abstract: This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performance not only in primitive AI compute, memory, and communication operations but also in executing sev… ▽ More This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performance not only in primitive AI compute, memory, and communication operations but also in executing several important AI workloads end-to-end. We then assess Gaudi NPU's programmability by discussing several software-level optimization strategies to employ for implementing critical FBGEMM operators and vLLM, evaluating their efficiency against GPU-optimized counterparts. Results indicate that Gaudi-2 achieves energy efficiency comparable to A100, though there are notable areas for improvement in terms of software maturity. Overall, we conclude that, with effective integration into high-level AI frameworks, Gaudi NPUs could challenge NVIDIA GPU's dominance in the AI server market, though further improvements are necessary to fully compete with NVIDIA's robust software ecosystem. △ Less

Submitted 21 March, 2025; v1 submitted 30 December, 2024; originally announced January 2025.

Comments: Accepted for publication at the 52nd IEEE/ACM International Symposium on Computer Architecture (ISCA-52), 2025

Unsupervised Parameter-free Outlier Detection using HDBSCAN* Outlier Profiles

Authors: Kushankur Ghosh, Murilo Coelho Naldi, Jörg Sander, Euijin Choo

Abstract: In machine learning and data mining, outliers are data points that significantly differ from the dataset and often introduce irrelevant information that can induce bias in its statistics and models. Therefore, unsupervised methods are crucial to detect outliers if there is limited or no information about them. Global-Local Outlier Scores based on Hierarchies (GLOSH) is an unsupervised outlier dete… ▽ More In machine learning and data mining, outliers are data points that significantly differ from the dataset and often introduce irrelevant information that can induce bias in its statistics and models. Therefore, unsupervised methods are crucial to detect outliers if there is limited or no information about them. Global-Local Outlier Scores based on Hierarchies (GLOSH) is an unsupervised outlier detection method within HDBSCAN*, a state-of-the-art hierarchical clustering method. GLOSH estimates outlier scores for each data point by comparing its density to the highest density of the region they reside in the HDBSCAN* hierarchy. GLOSH may be sensitive to HDBSCAN*'s minpts parameter that influences density estimation. With limited knowledge about the data, choosing an appropriate minpts value beforehand is challenging as one or some minpts values may better represent the underlying cluster structure than others. Additionally, in the process of searching for ``potential outliers'', one has to define the number of outliers n a dataset has, which may be impractical and is often unknown. In this paper, we propose an unsupervised strategy to find the ``best'' minpts value, leveraging the range of GLOSH scores across minpts values to identify the value for which GLOSH scores can best identify outliers from the rest of the dataset. Moreover, we propose an unsupervised strategy to estimate a threshold for classifying points into inliers and (potential) outliers without the need to pre-define any value. Our experiments show that our strategies can automatically find the minpts value and threshold that yield the best or near best outlier detection results using GLOSH. △ Less

Submitted 13 November, 2024; originally announced November 2024.

Comments: Accepted at IEEE International Conference on Big Data, IEEE BigData 2024

Atomic-scale 3D structural dynamics and functional degradation of Pt alloy nanocatalysts during the oxygen reduction reaction

Authors: Chaehwa Jeong, Juhyeok Lee, Hyesung Jo, KwangHo Lee, SangJae Lee, Colin Ophus, Peter Ercius, EunAe Cho, Yongsoo Yang

Abstract: Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their surface and subsurface. However, systema… ▽ More Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their surface and subsurface. However, systematic optimization remains incomplete, as it requires an atomic-scale understanding of these factors and their dynamics over potential cycling, as well as their relationship to ORR activity. Here, we implement neural network-assisted atomic electron tomography to measure the 3D atomic structural dynamics and their effects on the functional degradation of PtNi alloy catalysts. Our results reveal that PtNi catalysts undergo shape changes, surface alloying, and strain relaxation during cycling, which can be effectively mitigated by Ga doping. By combining geometry, local chemistry, and strain analysis, we calculated the changes in ORR activity over thousands of cycles and observed that Ga doping leads to higher initial activity and greater stability. These findings offer a pathway to understanding 3D atomic structural dynamics and their relation to ORR activity during cycling, paving the way for the systematic design of durable, high-efficiency nanocatalysts. △ Less

Submitted 28 August, 2025; v1 submitted 3 November, 2024; originally announced November 2024.

Comments: 66 pages, 4 main figures, 29 supplementary figures

Journal ref: Nature Communications 16, 8026 (2025)

Shining Light on the Dark Sector: Search for Axion-like Particles and Other New Physics in Photonic Final States with FASER

Authors: FASER collaboration, Roshan Mammen Abraham, Xiaocong Ai, John Anders, Claire Antel, Akitaka Ariga, Tomoko Ariga, Jeremy Atkinson, Florian U. Bernlochner, Emma Bianchi, Tobias Boeckh, Jamie Boyd, Lydia Brenner, Angela Burger, Franck Cadoux, Roberto Cardella, David W. Casper, Charlotte Cavanagh, Xin Chen, Eunhyung Cho, Dhruv Chouhan, Andrea Coccaro, Stephane Débieux, Monica D'Onofrio, Ansh Desai , et al. (84 additional authors not shown)

Abstract: The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are cha… ▽ More The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper. △ Less

Submitted 17 December, 2024; v1 submitted 14 October, 2024; originally announced October 2024.

Comments: 37 pages, 22 figures

Report number: CERN-EP-2024-262

3D-GSW: 3D Gaussian Splatting for Robust Watermarking

Authors: Youngdong Jang, Hyunje Park, Feng Yang, Heeju Ko, Euijin Choo, Sangpil Kim

Abstract: As 3D Gaussian Splatting (3D-GS) gains significant attention and its commercial usage increases, the need for watermarking technologies to prevent unauthorized use of the 3D-GS models and rendered images has become increasingly important. In this paper, we introduce a robust watermarking method for 3D-GS that secures copyright of both the model and its rendered images. Our proposed method remains… ▽ More As 3D Gaussian Splatting (3D-GS) gains significant attention and its commercial usage increases, the need for watermarking technologies to prevent unauthorized use of the 3D-GS models and rendered images has become increasingly important. In this paper, we introduce a robust watermarking method for 3D-GS that secures copyright of both the model and its rendered images. Our proposed method remains robust against distortions in rendered images and model attacks while maintaining high rendering quality. To achieve these objectives, we present Frequency-Guided Densification (FGD), which removes 3D Gaussians based on their contribution to rendering quality, enhancing real-time rendering and the robustness of the message. FGD utilizes Discrete Fourier Transform to split 3D Gaussians in high-frequency areas, improving rendering quality. Furthermore, we employ a gradient mask for 3D Gaussians and design a wavelet-subband loss to enhance rendering quality. Our experiments show that our method embeds the message in the rendered images invisibly and robustly against various attacks, including model distortion. Our method achieves superior performance in both rendering quality and watermark robustness while improving real-time rendering efficiency. Project page: https://kuai-lab.github.io/cvpr20253dgsw/ △ Less

Submitted 31 March, 2025; v1 submitted 20 September, 2024; originally announced September 2024.

A Disease-Specific Foundation Model Using Over 100K Fundus Images: Release and Validation for Abnormality and Multi-Disease Classification on Downstream Tasks

Authors: Boa Jang, Youngbin Ahn, Eun Kyung Choe, Chang Ki Yoon, Hyuk Jin Choi, Young-Gon Kim

Abstract: Artificial intelligence applied to retinal images offers significant potential for recognizing signs and symptoms of retinal conditions and expediting the diagnosis of eye diseases and systemic disorders. However, developing generalized artificial intelligence models for medical data often requires a large number of labeled images representing various disease signs, and most models are typically t… ▽ More Artificial intelligence applied to retinal images offers significant potential for recognizing signs and symptoms of retinal conditions and expediting the diagnosis of eye diseases and systemic disorders. However, developing generalized artificial intelligence models for medical data often requires a large number of labeled images representing various disease signs, and most models are typically task-specific, focusing on major retinal diseases. In this study, we developed a Fundus-Specific Pretrained Model (Image+Fundus), a supervised artificial intelligence model trained to detect abnormalities in fundus images. A total of 57,803 images were used to develop this pretrained model, which achieved superior performance across various downstream tasks, indicating that our proposed model outperforms other general methods. Our Image+Fundus model offers a generalized approach to improve model performance while reducing the number of labeled datasets required. Additionally, it provides more disease-specific insights into fundus images, with visualizations generated by our model. These disease-specific foundation models are invaluable in enhancing the performance and efficiency of deep learning models in the field of fundus imaging. △ Less

Submitted 16 August, 2024; originally announced August 2024.

Comments: 10 pages, 4 figures

Evidence of $h_{b}(\text{2P}) \to Υ(\text{1S})η$ decay and search for $h_{b}(\text{1P,2P}) \to Υ(\text{1S})π^0$ with the Belle detector

Authors: Belle Collaboration, E. Kovalenko, I. Adachi, H. Aihara, D. M. Asner, T. Aushev, R. Ayad, V. Babu, Sw. Banerjee, K. Belous, J. Bennett, M. Bessner, T. Bilka, D. Biswas, A. Bobrov, D. Bodrov, A. Bondar, A. Bozek, M. Bračko, P. Branchini, T. E. Browder, A. Budano, M. Campajola, M. -C. Chang, B. G. Cheon , et al. (142 additional authors not shown)

Abstract: We report the first evidence for the $h_{b}(\text{2P}) \to Υ(\text{1S})η$ transition with a significance of $3.5$ standard deviations. The decay branching fraction is measured to be $\mathcal{B}[h_{b}(\text{2P}) \to Υ(\text{1S})η]=(7.1 ~^{+3.7} _{-3.2}\pm 0.8)\times10^{-3}$, which is noticeably smaller than expected. We also set upper limits on $π^0$ transitions of… ▽ More We report the first evidence for the $h_{b}(\text{2P}) \to Υ(\text{1S})η$ transition with a significance of $3.5$ standard deviations. The decay branching fraction is measured to be $\mathcal{B}[h_{b}(\text{2P}) \to Υ(\text{1S})η]=(7.1 ~^{+3.7} _{-3.2}\pm 0.8)\times10^{-3}$, which is noticeably smaller than expected. We also set upper limits on $π^0$ transitions of $\mathcal{B}[h_{b}(\text{2P}) \to Υ(\text{1S})π^0] < 1.8\times10^{-3}$, and $\mathcal{B}[h_{b}(\text{1P})\to Υ(\text{1S})π^0] < 1.8\times10^{-3}$, at the $90\%$ confidence level. These results are obtained with a $131.4$~fb$^{-1}$ data sample collected near the $Υ(\text{5S})$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider. △ Less

Submitted 4 July, 2024; originally announced July 2024.

Comments: to be submitted to PRL

Report number: Belle Preprint 2024-03, KEK Preprint 2024-03

Study of $χ_{bJ}(2P)\toωΥ(1S)$ at Belle

Authors: Belle Collaboration, Z. S. Stottler, T. K. Pedlar, B. G. Fulsom, I. Adachi, K. Adamczyk, H. Aihara, S. Al Said, D. M. Asner, H. Atmacan, T. Aushev, R. Ayad, V. Babu, Sw. Banerjee, M. Bauer, P. Behera, K. Belous, J. Bennett, F. Bernlochner, M. Bessner, T. Bilka, D. Biswas, A. Bobrov, D. Bodrov, G. Bonvicini , et al. (157 additional authors not shown)

Abstract: We report a study of the hadronic transitions $χ_{bJ}(2P)\toωΥ(1S)$, with $ω\toπ^{+}π^{-}π^{0}$, using $28.2\times10^6~Υ(3S)$ mesons recorded by the Belle detector. We present the first evidence for the near--threshold transition $χ_{b0}(2P)\toωΥ(1S)$, the analog of the near-threshold charm sector decay $χ_{c1}(3872)\toωJ/ψ$, with a branching fraction of… ▽ More We report a study of the hadronic transitions $χ_{bJ}(2P)\toωΥ(1S)$, with $ω\toπ^{+}π^{-}π^{0}$, using $28.2\times10^6~Υ(3S)$ mesons recorded by the Belle detector. We present the first evidence for the near--threshold transition $χ_{b0}(2P)\toωΥ(1S)$, the analog of the near-threshold charm sector decay $χ_{c1}(3872)\toωJ/ψ$, with a branching fraction of $\cal{B}\big(χ_{b0}(2P)\toωΥ(1S)\big) = \big(0.55\pm0.19\pm0.07\big)\%$. We also obtain branching fractions of $\cal{B}\big(χ_{b1}(2P)\toωΥ(1S)\big) = \big(2.39{}^{+0.20}_{-0.19}\pm0.24\big)\%$ and $\cal{B}\big(χ_{b2}(2P)\toωΥ(1S)\big) = \big(0.47{}^{+0.13}_{-0.12}\pm0.06\big)\%$, confirming the measurement of the $ω$ transitions of the $J=1,2~P$--wave states. The ratio for the $J=2$ to $J=1$ transitions is also measured and found to differ by 3.3 standard deviations from the expected value in the QCD multipole expansion. △ Less

Submitted 23 July, 2025; v1 submitted 30 June, 2024; originally announced July 2024.

Comments: 7 pages, 2 figures

Report number: Belle Preprint: 2024-05; KEK Preprint: 2024-10

Search for charmed baryons in the $Λ_c^+η$ system and measurement of the branching fractions of $Λ_c(2880)^+$ and $Λ_c(2940)^+$ decaying to $Λ_c^+η$ and $pD^0$ relative to $Σ_c(2455)π$

Authors: Belle Collaboration, S. X. Li, C. P. Shen, I. Adachi, J. K. Ahn, H. Aihara, D. M. Asner, H. Atmacan, T. Aushev, R. Ayad, Sw. Banerjee, K. Belous, J. Bennett, M. Bessner, T. Bilka, D. Biswas, D. Bodrov, A. Bozek, M. Bračko, P. Branchini, T. E. Browder, A. Budano, M. Campajola, M. -C. Chang, B. G. Cheon , et al. (103 additional authors not shown)

Abstract: We search for excited charmed baryons in the $Λ_c^+η$ system using a data sample corresponding to an integrated luminosity of 980 $\rm fb^{-1}$. The data were collected by the Belle detector at the KEKB $e^{+}$$e^{-}$ asymmetric-energy collider. No significant signals are found in the $Λ_c^+η$ mass spectrum, including the known $Λ_c(2880)^+$ and $Λ_c(2940)^+$. Clear $Λ_c(2880)^+$ and… ▽ More We search for excited charmed baryons in the $Λ_c^+η$ system using a data sample corresponding to an integrated luminosity of 980 $\rm fb^{-1}$. The data were collected by the Belle detector at the KEKB $e^{+}$$e^{-}$ asymmetric-energy collider. No significant signals are found in the $Λ_c^+η$ mass spectrum, including the known $Λ_c(2880)^+$ and $Λ_c(2940)^+$. Clear $Λ_c(2880)^+$ and $Λ_c(2940)^+$ signals are observed in the $pD^0$ mass spectrum. We set upper limits at 90\% credibility level on ratios of branching fractions of $Λ_c(2880)^+$ and $Λ_c(2940)^+$ decaying to $Λ_c^+η$ relative to $Σ_c(2455)π$ of $<0.13$ for the $Λ_c(2880)^+$ and $<1.11$ for the $Λ_c(2940)^+$. We measure ratios of branching fractions of $Λ_c(2880)^+$ and $Λ_c(2940)^+$ decaying to $pD^0$ relative to $Σ_c(2455)π$ of $0.75 \pm 0.03(\text{stat.}) \pm 0.07(\text{syst.})$ for the $Λ_c(2880)^+$ and $3.59 \pm 0.21(\text{stat.}) \pm 0.56(\text{syst.})$ for the $Λ_c(2940)^+$. △ Less

Submitted 28 July, 2024; v1 submitted 22 June, 2024; originally announced June 2024.

Comments: 10 pages, 4 figures, accepted for publication as a Regular Article in Physical Review D

Report number: Belle Preprint: 2024-06;KEK Preprint: 2024-15

Journal ref: Phys. Rev. D 110, 032021 (2024)

Aligning Large Language Models with Diverse Political Viewpoints

Authors: Dominik Stammbach, Philine Widmer, Eunjung Cho, Caglar Gulcehre, Elliott Ash

Abstract: Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Models aligned with this data can generate more accurate political viewpoints from S… ▽ More Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Models aligned with this data can generate more accurate political viewpoints from Swiss parties, compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews summarizing multiple viewpoints using such models. The replication package contains all code and data. △ Less

Submitted 3 October, 2024; v1 submitted 20 June, 2024; originally announced June 2024.

Comments: accepted at EMNLP 2024 main as a short paper

BoA: Attention-aware Post-training Quantization without Backpropagation

Authors: Junhan Kim, Ho-young Kim, Eulrang Cho, Chungman Lee, Joonyoung Kim, Yongkweon Jeon

Abstract: Post-training quantization (PTQ) is a promising solution for deploying large language models (LLMs) on resource-constrained devices. Early methods developed for small-scale networks, such as ResNet, rely on gradient-based optimization, which becomes impractical for hyper-scale LLMs with billions of parameters. While recently proposed backpropagation-free or transformation-based methods alleviate t… ▽ More Post-training quantization (PTQ) is a promising solution for deploying large language models (LLMs) on resource-constrained devices. Early methods developed for small-scale networks, such as ResNet, rely on gradient-based optimization, which becomes impractical for hyper-scale LLMs with billions of parameters. While recently proposed backpropagation-free or transformation-based methods alleviate this issue, they ignore inter-layer interactions or use the naive nearest-rounding-based quantized weight assignment to save the heavy computational cost of weight optimization. In this paper, we introduce a novel backpropagation-free PTQ algorithm that optimizes quantized weights by considering inter-layer dependencies. The key innovation is the development of attention-aware Hessian matrices that capture inter-layer interactions within the attention module. Extensive experiments demonstrate that our approach not only outperforms existing weight quantization methods but also shows good synergy with conventional methods to suppress activation outliers, leading to state-of-the-art weight-activation quantization performance. The code will be available at https://github.com/SamsungLabs/BoA. △ Less

Submitted 6 June, 2025; v1 submitted 19 June, 2024; originally announced June 2024.

Comments: ICML 2025

DialSim: A Dialogue Simulator for Evaluating Long-Term Multi-Party Dialogue Understanding of Conversational Agents

Authors: Jiho Kim, Woosog Chay, Hyeonji Hwang, Daeun Kyung, Hyunseung Chung, Eunbyeol Cho, Yeonsu Kwon, Yohan Jo, Edward Choi

Abstract: Recent advancements in Large Language Models (LLMs) have significantly enhanced conversational agents, making them applicable to various fields (e.g., education, entertainment). Despite their progress, the evaluation of the agents often overlooks the complexities of real-world conversations, such as multi-party dialogues and extended contextual dependencies. To bridge this gap, we introduce DialSi… ▽ More Recent advancements in Large Language Models (LLMs) have significantly enhanced conversational agents, making them applicable to various fields (e.g., education, entertainment). Despite their progress, the evaluation of the agents often overlooks the complexities of real-world conversations, such as multi-party dialogues and extended contextual dependencies. To bridge this gap, we introduce DialSim, a dialogue simulation-based evaluation framework. In DialSim, an agent assumes the role of a character in a scripted conversation and is evaluated on their ability to answer spontaneous questions using only the dialogue history, while recognizing when they lack sufficient information. To support this framework, we introduce LongDialQA, a new QA dataset constructed from long-running TV shows, comprising over 1,300 dialogue sessions, each paired with more than 1,000 carefully curated questions, totaling over 352,000 tokens. To minimize reliance on prior knowledge, all character names are anonymized or swapped. Our evaluation of state-of-the-art LLM-based conversational agents using DialSim reveals that even models with large context windows or RAG capabilities struggle to maintain accurate comprehension over long-term, multi-party interactions-underscoring the need for more realistic and challenging benchmarks in conversational AI. △ Less

Submitted 25 September, 2025; v1 submitted 18 June, 2024; originally announced June 2024.

Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

Authors: Fujiao Ji, Kiho Lee, Hyungjoon Koo, Wenhao You, Euijin Choo, Hyoungshick Kim, Doowon Kim

Abstract: Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been underexplored. In this paper, we comprehensively scrutinize and evalu… ▽ More Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been underexplored. In this paper, we comprehensively scrutinize and evaluate the effectiveness and robustness of popular visual similarity-based anti-phishing models using a large-scale dataset of 451k real-world phishing websites. Our analyses of the effectiveness reveal that while certain visual similarity-based models achieve high accuracy on curated datasets in the experimental settings, they exhibit notably low performance on real-world datasets, highlighting the importance of real-world evaluation. Furthermore, we find that the attackers evade the detectors mainly in three ways: (1) directly attacking the model pipelines, (2) mimicking benign logos, and (3) employing relatively simple strategies such as eliminating logos from screenshots. To statistically assess the resilience and robustness of existing models against adversarial attacks, we categorize the strategies attackers employ into visible and perturbation-based manipulations and apply them to website logos. We then evaluate the models' robustness using these adversarial samples. Our findings reveal potential vulnerabilities in several models, emphasizing the need for more robust visual similarity techniques capable of withstanding sophisticated evasion attempts. We provide actionable insights for enhancing the security of phishing defense systems, encouraging proactive actions. △ Less

Submitted 29 January, 2025; v1 submitted 29 May, 2024; originally announced May 2024.

Comments: 14 pages

Three Disclaimers for Safe Disclosure: A Cardwriter for Reporting the Use of Generative AI in Writing Process

Authors: Won Ik Cho, Eunjung Cho, Hyeonji Shin

Abstract: Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being used in the academic writing process. This is despite the current lack of unified framework for reporting the use of machine assistance. In this work, we propose "Cardwriter", an intuitive interface that produces a short report for authors to declare their use of generative AI in their writing process.… ▽ More Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being used in the academic writing process. This is despite the current lack of unified framework for reporting the use of machine assistance. In this work, we propose "Cardwriter", an intuitive interface that produces a short report for authors to declare their use of generative AI in their writing process. The demo is available online, at https://cardwriter.vercel.app △ Less

Submitted 13 April, 2024; originally announced April 2024.

Comments: 6 pages; an implementation version of PaperCard project

Retrieval-Augmented Open-Vocabulary Object Detection

Authors: Jooyeon Kim, Eulrang Cho, Sehyung Kim, Hyunwoo J. Kim

Abstract: Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, iPod, and alligator. To extend the previous methods in two aspects, we propose R… ▽ More Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, iPod, and alligator. To extend the previous methods in two aspects, we propose Retrieval-Augmented Losses and visual Features (RALF). Our method retrieves related 'negative' classes and augments loss functions. Also, visual features are augmented with 'verbalized concepts' of classes, e.g., worn on the feet, handheld music player, and sharp teeth. Specifically, RALF consists of two modules: Retrieval Augmented Losses (RAL) and Retrieval-Augmented visual Features (RAF). RAL constitutes two losses reflecting the semantic similarity with negative vocabularies. In addition, RAF augments visual features with the verbalized concepts from a large language model (LLM). Our experiments demonstrate the effectiveness of RALF on COCO and LVIS benchmark datasets. We achieve improvement up to 3.4 box AP$_{50}^{\text{N}}$ on novel categories of the COCO dataset and 3.6 mask AP$_{\text{r}}$ gains on the LVIS dataset. Code is available at https://github.com/mlvlab/RALF . △ Less

Submitted 8 April, 2024; originally announced April 2024.

Comments: Accepted paper at CVPR 2024

Simplifying MBA Expression Using E-Graphs

Authors: Seoksu Lee, Hyeongchang Jeon, Eun-Sun Cho

Abstract: Code obfuscation involves the addition of meaningless code or the complication of existing code in order to make a program difficult to reverse engineer. In recent years, MBA (Mixed Boolean Arithmetic) obfuscation has been applied to virus and malware code to impede expert analysis. Among the various obfuscation techniques, Mixed Boolean Arithmetic (MBA) obfuscation is considered the most challeng… ▽ More Code obfuscation involves the addition of meaningless code or the complication of existing code in order to make a program difficult to reverse engineer. In recent years, MBA (Mixed Boolean Arithmetic) obfuscation has been applied to virus and malware code to impede expert analysis. Among the various obfuscation techniques, Mixed Boolean Arithmetic (MBA) obfuscation is considered the most challenging to decipher using existing code deobfuscation techniques. In this paper, we have attempted to simplify the MBA expression. We use an e-graph data structure to efficiently hold multiple expressions of the same semantics to systematically rewrite terms and find simpler expressions. The preliminary experimental result shows that our e-graph based MBA deobfuscation approach works faster with reasonable performance than other approaches do. △ Less

Submitted 8 April, 2024; originally announced April 2024.

HyperCLOVA X Technical Report

Authors: Kang Min Yoo, Jaegeun Han, Sookyo In, Heewon Jeon, Jisu Jeong, Jaewook Kang, Hyunwook Kim, Kyung-Min Kim, Munhyong Kim, Sungju Kim, Donghyun Kwak, Hanock Kwak, Se Jung Kwon, Bado Lee, Dongsoo Lee, Gichang Lee, Jooho Lee, Baeseong Park, Seongjin Shin, Joonsang Yu, Seolki Baek, Sumin Byeon, Eungsup Cho, Dooseok Choe, Jeesung Han , et al. (371 additional authors not shown)

Abstract: We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment t… ▽ More We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs. △ Less

Submitted 13 April, 2024; v1 submitted 2 April, 2024; originally announced April 2024.

Comments: 44 pages; updated authors list and fixed author names

Angular analysis of $B \to K^* e^+ e^-$ in the low-$q^2$ region with new electron identification at Belle

Authors: Belle Collaboration, D. Ferlewicz, P. Urquijo, I. Adachi, K. Adamczyk, H. Aihara, D. M. Asner, H. Atmacan, R. Ayad, V. Babu, Sw. Banerjee, P. Behera, K. Belous, J. Bennett, M. Bessner, V. Bhardwaj, B. Bhuyan, T. Bilka, D. Biswas, D. Bodrov, M. Bračko, P. Branchini, T. E. Browder, A. Budano, M. Campajola , et al. (145 additional authors not shown)

Abstract: We perform an angular analysis of the $B\to K^* e^+ e^-$ decay for the dielectron mass squared, $q^2$, range of $0.0008$ to $1.1200 ~\text{GeV}^2 /c^4$ using the full Belle data set in the $K^{*0} \to K^+ π^-$ and $K^{*+} \to K_S^0 π^+$ channels, incorporating new methods of electron identification to improve the statistical power of the data set. This analysis is sensitive to contributions from r… ▽ More We perform an angular analysis of the $B\to K^* e^+ e^-$ decay for the dielectron mass squared, $q^2$, range of $0.0008$ to $1.1200 ~\text{GeV}^2 /c^4$ using the full Belle data set in the $K^{*0} \to K^+ π^-$ and $K^{*+} \to K_S^0 π^+$ channels, incorporating new methods of electron identification to improve the statistical power of the data set. This analysis is sensitive to contributions from right-handed currents from physics beyond the Standard Model by constraining the Wilson coefficients $\mathcal{C}_7^{(\prime)}$. We perform a fit to the $B\to K^* e^+ e^-$ differential decay rate and measure the imaginary component of the transversality amplitude to be $A_T^{\rm Im} = -1.27 \pm 0.52 \pm 0.12$, and the $K^*$ transverse asymmetry to be $A_T^{(2)} = 0.52 \pm 0.53 \pm 0.11$, with $F_L$ and $A_T^{\rm Re}$ fixed to the Standard Model values. The resulting constraints on the value of $\mathcal{C}_7^{\prime}$ are consistent with the Standard Model within a $2σ$ confidence interval. △ Less

Submitted 11 September, 2024; v1 submitted 29 March, 2024; originally announced April 2024.

Comments: Accepted by PRD

Report number: Belle preprint 2023-20, KEK preprint 2023-38

Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks

Authors: Aqeel Anwar, Tae Eun Choe, Zian Wang, Sanja Fidler, Minwoo Park

Abstract: Detecting a diverse range of objects under various driving scenarios is essential for the effectiveness of autonomous driving systems. However, the real-world data collected often lacks the necessary diversity presenting a long-tail distribution. Although synthetic data has been utilized to overcome this issue by generating virtual scenes, it faces hurdles such as a significant domain gap and the… ▽ More Detecting a diverse range of objects under various driving scenarios is essential for the effectiveness of autonomous driving systems. However, the real-world data collected often lacks the necessary diversity presenting a long-tail distribution. Although synthetic data has been utilized to overcome this issue by generating virtual scenes, it faces hurdles such as a significant domain gap and the substantial efforts required from 3D artists to create realistic environments. To overcome these challenges, we present ARSim, a fully automated, comprehensive, modular framework designed to enhance real multi-view image data with 3D synthetic objects of interest. The proposed method integrates domain adaptation and randomization strategies to address covariate shift between real and simulated data by inferring essential domain attributes from real data and employing simulation-based randomization for other attributes. We construct a simplified virtual scene using real data and strategically place 3D synthetic assets within it. Illumination is achieved by estimating light distribution from multiple images capturing the surroundings of the vehicle. Camera parameters from real data are employed to render synthetic assets in each frame. The resulting augmented multi-view consistent dataset is used to train a multi-camera perception network for autonomous vehicles. Experimental results on various AV perception tasks demonstrate the superior performance of networks trained on the augmented dataset. △ Less

Submitted 22 March, 2024; originally announced March 2024.

Comments: 17 pages, 15 figures, 7 tables

Search for a pentaquark state decaying into $pJ/ψ$ in $Υ(1,2S)$ inclusive decays at Belle

Authors: Belle Collaboration, X. Dong, S. M. Zou, H. Y. Zhang, X. L. Wang, I. Adachi, J. K. Ahn, H. Aihara, S. Al Said, D. M. Asner, H. Atmacan, R. Ayad, S. Bahinipati, Sw. Banerjee, M. Bessner, V. Bhardwaj, D. Biswas, D. Bodrov, A. Bozek, M. Bračko, P. Branchini, T. E. Browder, A. Budano, M. Campajola, D. Červenkov , et al. (140 additional authors not shown)

Abstract: Using the data samples of 102 million $Υ(1S)$ and 158 million $Υ(2S)$ events collected by the Belle detector, we search for a pentaquark state in the $pJ/ψ$ final state from $Υ(1,2S)$ inclusive decays. Here, the charge-conjugate $\bar{p}J/ψ$ is included. We observe clear $pJ/ψ$ production in $Υ(1,2S)$ decays and measure the branching fractions to be… ▽ More Using the data samples of 102 million $Υ(1S)$ and 158 million $Υ(2S)$ events collected by the Belle detector, we search for a pentaquark state in the $pJ/ψ$ final state from $Υ(1,2S)$ inclusive decays. Here, the charge-conjugate $\bar{p}J/ψ$ is included. We observe clear $pJ/ψ$ production in $Υ(1,2S)$ decays and measure the branching fractions to be $B[Υ(1S) \to pJ/ψ+ anything] = [8.1 \pm 0.6(stat.) \pm 0.5(syst.)] \times 10^{-5}$ and $B[Υ(2S) \to pJ/ψ+ anything] = [4.3 \pm 0.5(stat.) \pm 0.4(syst.)] \times 10^{-5}$. We also measure the cross section of inclusive $pJ/ψ$ production in $e^+e^-$ annihilation to be $σ(e^+e^- \to pJ/ψ+ anything) = [108 \pm 11 (stat.) \pm 6(syst.)]$~fb at $\sqrt{s} = 10.52~\hbox{GeV}$ using an 89.5~fb$^{-1}$ continuum data sample. There is no significant $P_c(4312)^+$, $P_c(4440)^+$ or $P_c(4457)^+$ signal found in the $pJ/ψ$ final states in $Υ(1,2S)$ inclusive decays. We determine the upper limits of $B[Υ(1,2S)\to P_c^{+} + anything] \cdot B(P_c^{+}\to pJ/ψ)$ to be at the $10^{-6}$ level. △ Less

Submitted 8 August, 2025; v1 submitted 7 March, 2024; originally announced March 2024.

Report number: Belle Preprint 2024-02, KEK Preprint 2023-54

Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease

Authors: So Yeon Kim, Sehee Wang, Eun Kyung Choe

Abstract: Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness… ▽ More Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion of human-centric explanations through explainable GNNs, providing personalized feature importance scores for enhanced interpretability and clinical relevance, thereby underscoring the potential of our approach in advancing healthcare practices with a keen focus on graph representation learning and human-centric explanation. △ Less

Submitted 5 March, 2024; originally announced March 2024.

Comments: Paper accepted in Human-Centric Representation Learning workshop at AAAI 2024 (https://hcrl-workshop.github.io/2024/)

Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers

Authors: Junhan Kim, Chungman Lee, Eulrang Cho, Kyungphil Park, Ho-young Kim, Joonyoung Kim, Yongkweon Jeon

Abstract: With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile and TVs. Existing PTQ schemes, however, consume considerable time and resources, which could be a bottleneck in real situations where frequent model updates and multiple hyperparameter tunings are required. As a… ▽ More With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile and TVs. Existing PTQ schemes, however, consume considerable time and resources, which could be a bottleneck in real situations where frequent model updates and multiple hyperparameter tunings are required. As a cost-effective alternative, learning-free PTQ schemes have been proposed. However, the performance is somewhat limited because they cannot consider the inter-layer dependency within the attention module, which is a significant feature of Transformers. In this paper, we thus propose a novel PTQ algorithm that balances accuracy and efficiency. The key idea of the proposed algorithm called aespa is to perform quantization layer-wise for efficiency while targeting attention-wise reconstruction to consider the cross-layer dependency. Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models. △ Less

Submitted 5 November, 2024; v1 submitted 14 February, 2024; originally announced February 2024.

Comments: Accepted to NeurIPS 2024

Search for a heavy neutral lepton that mixes predominantly with the tau neutrino

Authors: Belle Collaboration, M. Nayak, S. Dey, A. Soffer, I. Adachi, H. Aihara, S. Al Said, D. M. Asner, H. Atmacan, R. Ayad, V. Babu, Sw. Banerjee, M. Bauer, P. Behera, K. Belous, M. Bessner, V. Bhardwaj, B. Bhuyan, T. Bilka, D. Biswas, A. Bobrov, D. Bodrov, M. Bračko, P. Branchini, T. E. Browder , et al. (143 additional authors not shown)

Abstract: We report a search for a heavy neutral lepton (HNL) that mixes predominantly with $ν_τ$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy $e^+ e^-$ collider. The data sample was collected at and just below the center-of-mass energies of the $Υ(4S)$ and $Υ(5S)$ resonances and has an integrated luminosity of $915~\textrm{fb}^{-1}$, corresponding to… ▽ More We report a search for a heavy neutral lepton (HNL) that mixes predominantly with $ν_τ$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy $e^+ e^-$ collider. The data sample was collected at and just below the center-of-mass energies of the $Υ(4S)$ and $Υ(5S)$ resonances and has an integrated luminosity of $915~\textrm{fb}^{-1}$, corresponding to $(836\pm 12)\times 10^6$ $e^+e^\toτ^+τ^-$ events. We search for production of the HNL (denoted $N$) in the decay $τ^-\to π^- N$ followed by its decay via $N \to μ^+μ^- ν_τ$. The search focuses on the parameter-space region in which the HNL is long lived, so that the $μ^+μ^-$ originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the $τ$ neutrino as a function of the HNL mass. △ Less

Submitted 14 June, 2024; v1 submitted 4 February, 2024; originally announced February 2024.

Comments: 10 pages, 5 figures

Report number: Belle Preprint 2023-22, KEK Preprint 2023-44

Journal ref: Phys.Rev.D 109 (2024) 11, L111102

The forb-flex method for odd coloring and proper conflict-free coloring of planar graphs

Authors: James Anderson, Herman Chau, Eun-Kyung Cho, Nicholas Crawford, Stephen G. Hartke, Emily Heath, Owen Henderschedt, Hyemin Kwon, Zhiyuan Zhang

Abstract: We introduce a new tool useful for greedy coloring, which we call the forb-flex method, and apply it to odd coloring and proper conflict-free coloring of planar graphs. The odd chromatic number, denoted $χ_{\mathsf{o}}(G)$, is the smallest number of colors needed to properly color $G$ such that every non-isolated vertex of $G$ has a color appearing an odd number of times in its neighborhood. The p… ▽ More We introduce a new tool useful for greedy coloring, which we call the forb-flex method, and apply it to odd coloring and proper conflict-free coloring of planar graphs. The odd chromatic number, denoted $χ_{\mathsf{o}}(G)$, is the smallest number of colors needed to properly color $G$ such that every non-isolated vertex of $G$ has a color appearing an odd number of times in its neighborhood. The proper conflict-free chromatic number, denoted $χ_{\mathsf{PCF}}(G)$, is the smallest number of colors needed to properly color $G$ such that every non-isolated vertex of $G$ has a color appearing uniquely in its neighborhood. Our new tool works by carefully counting the structures in the neighborhood of a vertex and determining if a neighbor of a vertex can be recolored at the end of a greedy coloring process to avoid conflicts. Combining this with the discharging method allows us to prove $χ_{\mathsf{PCF}}(G) \leq 4$ for planar graphs of girth at least 11, and $χ_{\mathsf{o}}(G) \leq 4$ for planar graphs of girth at least 10. These results improve upon the recent works of Cho, Choi, Kwon, and Park. △ Less

Submitted 25 January, 2024; originally announced January 2024.

Comments: 32 pages, 11 figures

Measurements of the branching fraction, polarization, and $CP$ asymmetry for the decay $B^0\rightarrow ωω$

Authors: Belle Collaboration, Y. Guan, A. J. Schwartz, K. Kinoshita, I. Adachi, H. Aihara, S. Al Said, D. M. Asner, H. Atmacan, R. Ayad, S. Bahinipati, Sw. Banerjee, K. Belous, J. Bennett, M. Bessner, V. Bhardwaj, B. Bhuyan, D. Biswas, A. Bobrov, D. Bodrov, J. Borah, A. Bozek, M. Bračko, P. Branchini, A. Budano , et al. (145 additional authors not shown)

Abstract: We present a measurement of $B^{0} \rightarrow ωω$, a charmless decay into two vector mesons, using 772 $\times 10^6$ $B\overline{B}$ pairs collected with the Belle detector at the KEKB $e^+e^-$ collider. The decay is observed with a significance of 7.9 standard deviations. We measure a branching fraction $\mathcal{B} = (1.53 \pm 0.29 \pm 0.17) \times 10^{-6}$, a fraction of longitudinal polarizat… ▽ More We present a measurement of $B^{0} \rightarrow ωω$, a charmless decay into two vector mesons, using 772 $\times 10^6$ $B\overline{B}$ pairs collected with the Belle detector at the KEKB $e^+e^-$ collider. The decay is observed with a significance of 7.9 standard deviations. We measure a branching fraction $\mathcal{B} = (1.53 \pm 0.29 \pm 0.17) \times 10^{-6}$, a fraction of longitudinal polarization $f_L = 0.87 \pm 0.13 \pm 0.13$, and a time-integrated $CP$ asymmetry $A_{CP}$ = $-0.44 \pm 0.43 \pm 0.11$, where the first uncertainties listed are statistical and the second are systematic. This is the first observation of $B^{0} \rightarrow ωω$, and the first measurements of $f_L$ and $A_{CP}$ for this decay. △ Less

Submitted 9 January, 2024; originally announced January 2024.

Comments: 7 pages, 1 figure, submitted to Phys. Rev. Lett

Report number: Belle Preprint 2023-21, KEK Preprint 2023-43, UCHEP-24-01